import gym
import torch
import torch.nn.functional as F
import numpy as np
import matplotlib.pyplot as plt
import rl_utils

class PolicyNet(torch.nn.Module):
    '''策略网络'''
    def __init__(self,state_dim,hidden_dim,action_dim):
        super(PolicyNet,self).__init__()
        self.fc1 = torch.nn.Linear(state_dim,hidden_dim)
        self.fc2 = torch.nn.Linear(hidden_dim,action_dim)
    def forward(self,x):
        x = F.relu(self.fc1(x))
        return F.softmax(self.fc2(x),dim=1)

class ValueNet(torch.nn.Module):
    '''价值网络'''
    def __init__(self,state_dim,hidden_dim):
        super(ValueNet,self).__init__()
        self.fc1 = torch.nn.Linear(state_dim,hidden_dim)
        self.fc2 = torch.nn.Linear(hidden_dim,1)
    def forward(self,x):
        x = F.relu(self.fc1(x))
        return self.fc2(x)

class ActorCritic:
    def __init__(self,state_dim,hidden_dim,action_dim,actor_lr,critic_lr,gamma,device):
        self.actor = PolicyNet(state_dim,hidden_dim,action_dim).to(device) # 策略网络
        self.critic = ValueNet(state_dim,hidden_dim).to(device) # 价值网络

        self.actor_optimizer = torch.optim.Adam(self.actor.parameters(),lr=actor_lr) # 策略网络优化器
        self.critic_optimizer = torch.optim.Adam(self.critic.parameters(),lr=critic_lr) # 价值网络优化器

        self.gamma = gamma
        self.device = device

    def take_action(self,state):
        state = torch.tensor([state],dtype=torch.float).to(self.device)
        probs = self.actor(state)
        action_dist = torch.distributions.Categorical(probs)
        action = action_dist.sample()
        return action.item()

    def update(self,transition_dict):
        states = torch.tensor(transition_dict['states'],dtype=torch.float).to(self.device)
        actions = torch.tensor(transition_dict['actions']).view(-1,1).to(self.device)
        rewards = torch.tensor(transition_dict['rewards'],dtype=torch.float).view(-1,1).to(self.device)
        next_states = torch.tensor(transition_dict['next_states'],dtype=torch.float).to(self.device)
        dones = torch.tensor(transition_dict['dones'],dtype=torch.float).view(-1,1).to(self.device)

        # 时许差分目标
        td_target = rewards + self.gamma * self.critic(next_states) * (1-dones)
        # 时序差分误差
        td_delta = td_target - self.critic(states)

        log_probs = torch.log(self.actor(states).gather(1,actions))
        actor_loss = torch.mean(-log_probs * td_delta.detach())
        critic_loss = torch.mean(F.mse_loss(self.critic(states),td_target.detach()))

        self.actor_optimizer.zero_grad()
        self.critic_optimizer.zero_grad()
        actor_loss.backward()
        critic_loss.backward()
        self.actor_optimizer.step()
        self.critic_optimizer.step()

if __name__ == '__main__':
    actor_lr = 1e-3
    critic_lr = 1e-2
    num_episodes = 1000
    hidden_dim = 128
    gamma = 0.98
    device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")

    env_name = 'CartPole-v0'
    env = gym.make(env_name)
    env.seed(0)
    torch.manual_seed(0)

    state_dim = env.observation_space.shape[0]
    action_dim = env.action_space.n

    agent = ActorCritic(state_dim,hidden_dim,action_dim,actor_lr,critic_lr,gamma,device)
    return_list = rl_utils.train_on_policy_agent(env,agent,num_episodes)

    episodes_list = list(range(len(return_list)))
    plt.plot(episodes_list,return_list)
    plt.xlabel('Episodes')
    plt.ylabel('Returns')
    plt.title('Actor-Critic on {}'.format(env_name))
    plt.show()

    mv_return = rl_utils.moving_average(return_list,9)
    plt.plot(episodes_list,mv_return)
    plt.xlabel('Episodes')
    plt.ylabel('Returns')
    plt.title('Actor-Critic on {}'.format(env_name))
    plt.show()